Machine Learning-Driven Personalized Risk Prediction: Developing an Explainable Sarcopenia Model for Older European Adults with Arthritis.

ABSTRACT: Objectives: This study aimed to develop and validate an explainable machine learning (ML) model to predict the risk of sarcopenia in older European adults with arthritis, providing a practical tool for early and precise screening in clinical settings. Methods: We analyzed data from the English Longitudinal Study of Aging (ELSA) and the Survey of Health, Aging and Retirement in Europe (SHARE). The final analysis included 1959 participants aged ≥65 years. The ELSA dataset was divided into a training set (n = 1371) and an internal validation set (n = 588), while the SHARE dataset (n = 1001) served as an independent external test cohort. From an initial pool of 33 variables, nine core predictors were identified using ensemble feature selection techniques. Six ML algorithms were c

SUBMITTER: Xu X 

PROVIDER: S-EPMC12897810 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature